Prediction of Body Weight from Linear Body Measurements in Kashmiri (Kashir) Goat

Abstract

Background: Kashmiri (Kashir) goat is nondescript and highly variable, medium sized, goat genetic resource of Kashmir. The goat possessing excellent survivability and is distributed in Kashmir valley. In Jammu and Kashmir, Gujjars and Bakerwals have adopted the sheep and goat rearing as their primary occupation. Body weights at different ages of life cycle of animals are important traits owing to their direct relation with farmers income. Methods: Data on body weight and body measurements viz ear length (EL), body length (BL), body height at wither (BH), chest girth (CG) and tail length (TL) of 162 adult Kasmiri (Kashir) nondescript goats, irrespective of gender, were collected from Febuary 2019 to January 2020 to estimate the body weight from body measurements. The data were subjected to standard statistical analysis using SPSS software. Descriptive statistics and Pearson correlation was worked out to observe association among traits under study. The linear regression was utilized to predict body weight from linear body measurements. Result: The average body weight (BW), ear length (EL), body length (BL), body height at wither (BH), chest girth (CG) and tail length (TL) of 36.94±0.99 kg, 13.13±0.36 cm, 55.48±0.43 cm, 65.93±0.62 cm, 69.61±0.73 cm and 12.94±0.15 cm, respectively were observed for Kashmiri goat in the present study. The body weight was having positive and significant correlations with BL, HW, CG and TL. The correlation of body weight with EL and TL was low. Similarly the correlation of body weight with HW was moderate whereas with BL and CG was significantly high indicating these two traits can define body weight of animal more accurately. Based on R2 (coefficient of determination) criteria, the best regression model for predicting body weight of Kashmiri goat was obtained by using combination of BL (body length) and CG (chest girth) with R2 (coefficient of determination) of 0.795. Prediction equation with R2 value of 0.806 was obtained when all traits were included in regression model where as prediction equation with R2 value of 0.589 was obtained when only chest girth was used as independent variable.